Overview

Dataset statistics

Number of variables70
Number of observations15207
Missing cells2019
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.0 MiB
Average record size in memory553.0 B

Variable types

Numeric5
Categorical64
Boolean1

Alerts

CURRENT_SIZE_OF_PRODUCT has a high cardinality: 327 distinct valuesHigh cardinality
product_size has a high cardinality: 319 distinct valuesHigh cardinality
pct_disc is highly overall correlated with pct_retail_discHigh correlation
pct_retail_disc is highly overall correlated with pct_discHigh correlation
marital_status_A is highly overall correlated with hhsize_1High correlation
hhcomp_2 Adults Kids is highly overall correlated with kid_category_1 and 1 other fieldsHigh correlation
hhcomp_2 Adults No Kids is highly overall correlated with hhsize_2High correlation
kid_category_1 is highly overall correlated with hhcomp_2 Adults Kids and 2 other fieldsHigh correlation
kid_category_2 is highly overall correlated with hhsize_4High correlation
kid_category_3+ is highly overall correlated with hhsize_5+High correlation
kid_category_None/Unknown is highly overall correlated with hhcomp_2 Adults Kids and 2 other fieldsHigh correlation
age_35-44 is highly overall correlated with age_45-54High correlation
age_45-54 is highly overall correlated with age_35-44High correlation
hhsize_1 is highly overall correlated with marital_status_AHigh correlation
hhsize_2 is highly overall correlated with hhcomp_2 Adults No KidsHigh correlation
hhsize_3 is highly overall correlated with kid_category_1 and 1 other fieldsHigh correlation
hhsize_4 is highly overall correlated with kid_category_2High correlation
hhsize_5+ is highly overall correlated with kid_category_3+High correlation
campaign_13.0 is highly overall correlated with description_TypeAHigh correlation
campaign_18.0 is highly overall correlated with description_TypeAHigh correlation
description_TypeA is highly overall correlated with campaign_13.0 and 1 other fieldsHigh correlation
display_1 is highly imbalanced (63.7%)Imbalance
display_2 is highly imbalanced (88.6%)Imbalance
display_3 is highly imbalanced (87.1%)Imbalance
display_4 is highly imbalanced (94.8%)Imbalance
display_5 is highly imbalanced (84.1%)Imbalance
display_6 is highly imbalanced (94.9%)Imbalance
display_7 is highly imbalanced (78.4%)Imbalance
display_9 is highly imbalanced (87.6%)Imbalance
display_A is highly imbalanced (94.5%)Imbalance
mailer_C is highly imbalanced (99.8%)Imbalance
mailer_D is highly imbalanced (93.4%)Imbalance
mailer_F is highly imbalanced (99.8%)Imbalance
mailer_H is highly imbalanced (84.9%)Imbalance
mailer_J is highly imbalanced (96.9%)Imbalance
mailer_L is highly imbalanced (99.8%)Imbalance
homeowner_Probable Owner is highly imbalanced (87.5%)Imbalance
homeowner_Probable Renter is highly imbalanced (91.0%)Imbalance
homeowner_Renter is highly imbalanced (64.3%)Imbalance
hhcomp_1 Adult Kids is highly imbalanced (64.6%)Imbalance
hhcomp_Single Male is highly imbalanced (55.1%)Imbalance
kid_category_2 is highly imbalanced (51.6%)Imbalance
age_19-24 is highly imbalanced (72.2%)Imbalance
age_55-64 is highly imbalanced (69.7%)Imbalance
age_65+ is highly imbalanced (64.2%)Imbalance
income_100-124K is highly imbalanced (75.3%)Imbalance
income_125-149K is highly imbalanced (67.2%)Imbalance
income_15-24K is highly imbalanced (60.7%)Imbalance
income_150-174K is highly imbalanced (75.4%)Imbalance
income_175-199K is highly imbalanced (87.5%)Imbalance
income_200-249K is highly imbalanced (96.4%)Imbalance
income_25-34K is highly imbalanced (53.1%)Imbalance
income_250K+ is highly imbalanced (83.5%)Imbalance
income_Under 15K is highly imbalanced (61.4%)Imbalance
hhsize_4 is highly imbalanced (56.3%)Imbalance
hhsize_5+ is highly imbalanced (51.7%)Imbalance
campaign_8.0 is highly imbalanced (98.1%)Imbalance
campaign_13.0 is highly imbalanced (98.0%)Imbalance
campaign_18.0 is highly imbalanced (97.1%)Imbalance
campaign_25.0 is highly imbalanced (99.9%)Imbalance
campaign_26.0 is highly imbalanced (99.5%)Imbalance
campaign_30.0 is highly imbalanced (99.9%)Imbalance
description_TypeA is highly imbalanced (93.9%)Imbalance
description_TypeB is highly imbalanced (99.9%)Imbalance
product_size has 2019 (13.3%) missing valuesMissing
Unnamed: 0 has unique valuesUnique
pct_disc has 8895 (58.5%) zerosZeros
pct_retail_disc has 8962 (58.9%) zerosZeros
pct_coupon_disc has 15099 (99.3%) zerosZeros

Reproduction

Analysis started2023-05-23 12:21:17.486202
Analysis finished2023-05-23 12:21:45.523782
Duration28.04 seconds
Software versionpandas-profiling v0.0.dev0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

Distinct15207
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13935.074
Minimum0
Maximum27405
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size118.9 KiB
2023-05-23T14:21:45.836745image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1400.3
Q17024.5
median13985
Q321252.5
95-th percentile26197.7
Maximum27405
Range27405
Interquartile range (IQR)14228

Descriptive statistics

Standard deviation8103.2073
Coefficient of variation (CV)0.58149724
Kurtosis-1.2672645
Mean13935.074
Median Absolute Deviation (MAD)7205
Skewness-0.022442935
Sum2.1191068 × 108
Variance65661969
MonotonicityStrictly increasing
2023-05-23T14:21:46.010207image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
18832 1
 
< 0.1%
18820 1
 
< 0.1%
18821 1
 
< 0.1%
18822 1
 
< 0.1%
18823 1
 
< 0.1%
18824 1
 
< 0.1%
18825 1
 
< 0.1%
18826 1
 
< 0.1%
18827 1
 
< 0.1%
Other values (15197) 15197
99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
27405 1
< 0.1%
27404 1
< 0.1%
27403 1
< 0.1%
27402 1
< 0.1%
27401 1
< 0.1%
27400 1
< 0.1%
27399 1
< 0.1%
27398 1
< 0.1%
27397 1
< 0.1%
27396 1
< 0.1%
Distinct327
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
2019 
12 OZ
 
866
4.4 OZ
 
749
4.25 OZ
 
591
11 OZ
 
571
Other values (322)
10411 

Length

Max length10
Median length8
Mean length5.0921286
Min length1

Characters and Unicode

Total characters77436
Distinct characters32
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique56 ?
Unique (%)0.4%

Sample

1st row
2nd row18 OZ
3rd row6 OZ
4th row6 OZ
5th row3.5 OZ

Common Values

ValueCountFrequency (%)
2019
 
13.3%
12 OZ 866
 
5.7%
4.4 OZ 749
 
4.9%
4.25 OZ 591
 
3.9%
11 OZ 571
 
3.8%
14 OZ 571
 
3.8%
13 OZ 504
 
3.3%
4.5 OZ 446
 
2.9%
16 OZ 436
 
2.9%
5 OZ 269
 
1.8%
Other values (317) 8185
53.8%

Length

2023-05-23T14:21:46.151814image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
oz 11816
44.9%
12 1285
 
4.9%
4.4 749
 
2.8%
14 638
 
2.4%
4.25 591
 
2.2%
11 586
 
2.2%
13 535
 
2.0%
pk 528
 
2.0%
6 457
 
1.7%
4.5 447
 
1.7%
Other values (282) 8661
32.9%

Most occurring characters

ValueCountFrequency (%)
15124
19.5%
O 12133
15.7%
Z 11948
15.4%
1 7493
9.7%
. 6511
8.4%
4 4586
 
5.9%
2 4263
 
5.5%
5 3893
 
5.0%
3 2036
 
2.6%
6 2009
 
2.6%
Other values (22) 7440
9.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28513
36.8%
Uppercase Letter 27194
35.1%
Space Separator 15124
19.5%
Other Punctuation 6605
 
8.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 12133
44.6%
Z 11948
43.9%
P 794
 
2.9%
C 670
 
2.5%
K 633
 
2.3%
T 359
 
1.3%
U 183
 
0.7%
N 163
 
0.6%
E 153
 
0.6%
B 44
 
0.2%
Other values (9) 114
 
0.4%
Decimal Number
ValueCountFrequency (%)
1 7493
26.3%
4 4586
16.1%
2 4263
15.0%
5 3893
13.7%
3 2036
 
7.1%
6 2009
 
7.0%
7 1312
 
4.6%
8 1206
 
4.2%
0 1086
 
3.8%
9 629
 
2.2%
Other Punctuation
ValueCountFrequency (%)
. 6511
98.6%
/ 94
 
1.4%
Space Separator
ValueCountFrequency (%)
15124
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 50242
64.9%
Latin 27194
35.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 12133
44.6%
Z 11948
43.9%
P 794
 
2.9%
C 670
 
2.5%
K 633
 
2.3%
T 359
 
1.3%
U 183
 
0.7%
N 163
 
0.6%
E 153
 
0.6%
B 44
 
0.2%
Other values (9) 114
 
0.4%
Common
ValueCountFrequency (%)
15124
30.1%
1 7493
14.9%
. 6511
13.0%
4 4586
 
9.1%
2 4263
 
8.5%
5 3893
 
7.7%
3 2036
 
4.1%
6 2009
 
4.0%
7 1312
 
2.6%
8 1206
 
2.4%
Other values (3) 1809
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 77436
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
15124
19.5%
O 12133
15.7%
Z 11948
15.4%
1 7493
9.7%
. 6511
8.4%
4 4586
 
5.9%
2 4263
 
5.5%
5 3893
 
5.0%
3 2036
 
2.6%
6 2009
 
2.6%
Other values (22) 7440
9.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.0 KiB
True
10846 
False
4361 
ValueCountFrequency (%)
True 10846
71.3%
False 4361
28.7%
2023-05-23T14:21:46.292778image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

shelf_price
Real number (ℝ)

Distinct287
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1785747
Minimum0.1
Maximum57.57
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size118.9 KiB
2023-05-23T14:21:46.418158image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.5
Q11.39
median1.79
Q32.79
95-th percentile4.79
Maximum57.57
Range57.47
Interquartile range (IQR)1.4

Descriptive statistics

Standard deviation1.6888433
Coefficient of variation (CV)0.77520559
Kurtosis96.385061
Mean2.1785747
Median Absolute Deviation (MAD)0.71
Skewness5.6167503
Sum33129.586
Variance2.8521918
MonotonicityNot monotonic
2023-05-23T14:21:46.732164image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.49 2332
15.3%
1.99 1276
 
8.4%
0.99 1261
 
8.3%
2.89 859
 
5.6%
2.99 741
 
4.9%
1.79 675
 
4.4%
3.19 647
 
4.3%
1.5 613
 
4.0%
2.5 537
 
3.5%
1.39 513
 
3.4%
Other values (277) 5753
37.8%
ValueCountFrequency (%)
0.1 5
 
< 0.1%
0.125 1
 
< 0.1%
0.15 1
 
< 0.1%
0.2 1
 
< 0.1%
0.2 11
 
0.1%
0.25 143
0.9%
0.27 1
 
< 0.1%
0.28 2
 
< 0.1%
0.3 1
 
< 0.1%
0.32 1
 
< 0.1%
ValueCountFrequency (%)
57.57 1
 
< 0.1%
27.99 1
 
< 0.1%
21.55 1
 
< 0.1%
19.99 3
 
< 0.1%
19.49 5
< 0.1%
15.99 3
 
< 0.1%
14.99 11
0.1%
12.49 4
 
< 0.1%
12.29 4
 
< 0.1%
11.99 2
 
< 0.1%

pct_disc
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct433
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1303918
Minimum0
Maximum0.93079585
Zeros8895
Zeros (%)58.5%
Negative0
Negative (%)0.0%
Memory size118.9 KiB
2023-05-23T14:21:46.920735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.32663317
95-th percentile0.49748744
Maximum0.93079585
Range0.93079585
Interquartile range (IQR)0.32663317

Descriptive statistics

Standard deviation0.17937756
Coefficient of variation (CV)1.3756813
Kurtosis0.47398231
Mean0.1303918
Median Absolute Deviation (MAD)0
Skewness1.1477682
Sum1982.8681
Variance0.03217631
MonotonicityNot monotonic
2023-05-23T14:21:47.062652image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8895
58.5%
0.3288590604 1889
 
12.4%
0.1349480969 338
 
2.2%
0.2805755396 269
 
1.8%
0.3333333333 164
 
1.1%
0.5 158
 
1.0%
0.4949494949 148
 
1.0%
0.1039426523 143
 
0.9%
0.4974874372 120
 
0.8%
0.2163009404 107
 
0.7%
Other values (423) 2976
 
19.6%
ValueCountFrequency (%)
0 8895
58.5%
0.007222222222 1
 
< 0.1%
0.01005025126 6
 
< 0.1%
0.02044989775 1
 
< 0.1%
0.03474903475 9
 
0.1%
0.04784688995 3
 
< 0.1%
0.04938271605 1
 
< 0.1%
0.05291005291 1
 
< 0.1%
0.05527638191 19
 
0.1%
0.05917159763 3
 
< 0.1%
ValueCountFrequency (%)
0.9307958478 1
< 0.1%
0.8743718593 1
< 0.1%
0.8341708543 1
< 0.1%
0.8327759197 2
< 0.1%
0.8316498316 1
< 0.1%
0.8305084746 1
< 0.1%
0.8269896194 2
< 0.1%
0.8207885305 1
< 0.1%
0.797979798 1
< 0.1%
0.7883597884 1
< 0.1%

pct_retail_disc
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct382
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12800657
Minimum-0
Maximum0.93079585
Zeros8962
Zeros (%)58.9%
Negative0
Negative (%)0.0%
Memory size118.9 KiB
2023-05-23T14:21:47.219287image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0
5-th percentile0
Q1-0
median0
Q30.28434504
95-th percentile0.49748744
Maximum0.93079585
Range0.93079585
Interquartile range (IQR)0.28434504

Descriptive statistics

Standard deviation0.17757929
Coefficient of variation (CV)1.3872669
Kurtosis0.52178453
Mean0.12800657
Median Absolute Deviation (MAD)0
Skewness1.1643207
Sum1946.596
Variance0.031534403
MonotonicityNot monotonic
2023-05-23T14:21:47.360707image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0 8962
58.9%
0.3288590604 1853
 
12.2%
0.1349480969 339
 
2.2%
0.2805755396 274
 
1.8%
0.3333333333 164
 
1.1%
0.5 154
 
1.0%
0.4949494949 151
 
1.0%
0.1039426523 147
 
1.0%
0.4974874372 124
 
0.8%
0.2163009404 107
 
0.7%
Other values (372) 2932
 
19.3%
ValueCountFrequency (%)
-0 8962
58.9%
0.007222222222 1
 
< 0.1%
0.01005025126 6
 
< 0.1%
0.02044989775 1
 
< 0.1%
0.03474903475 9
 
0.1%
0.04784688995 3
 
< 0.1%
0.04938271605 1
 
< 0.1%
0.05291005291 1
 
< 0.1%
0.05527638191 19
 
0.1%
0.05917159763 3
 
< 0.1%
ValueCountFrequency (%)
0.9307958478 1
< 0.1%
0.8743718593 1
< 0.1%
0.8341708543 1
< 0.1%
0.8327759197 2
< 0.1%
0.8305084746 1
< 0.1%
0.8269896194 2
< 0.1%
0.797979798 1
< 0.1%
0.7883597884 1
< 0.1%
0.7759197324 1
< 0.1%
0.76 1
< 0.1%

pct_coupon_disc
Real number (ℝ)

Distinct43
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0023852234
Minimum-0
Maximum0.71684588
Zeros15099
Zeros (%)99.3%
Negative0
Negative (%)0.0%
Memory size118.9 KiB
2023-05-23T14:21:47.501813image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0
5-th percentile0
Q1-0
median0
Q3-0
95-th percentile-0
Maximum0.71684588
Range0.71684588
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.030190347
Coefficient of variation (CV)12.657241
Kurtosis212.38719
Mean0.0023852234
Median Absolute Deviation (MAD)0
Skewness14.021251
Sum36.272092
Variance0.00091145703
MonotonicityNot monotonic
2023-05-23T14:21:47.611628image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
-0 15099
99.3%
0.3344481605 13
 
0.1%
0.3134796238 10
 
0.1%
0.5025125628 9
 
0.1%
0.2602230483 9
 
0.1%
0.4 7
 
< 0.1%
0.4366812227 7
 
< 0.1%
0.2 6
 
< 0.1%
0.5 4
 
< 0.1%
0.2132196162 3
 
< 0.1%
Other values (33) 40
 
0.3%
ValueCountFrequency (%)
-0 15099
99.3%
0.1002004008 1
 
< 0.1%
0.119474313 1
 
< 0.1%
0.1251564456 1
 
< 0.1%
0.1333333333 2
 
< 0.1%
0.1432664756 1
 
< 0.1%
0.1474926254 1
 
< 0.1%
0.1519756839 1
 
< 0.1%
0.159453303 1
 
< 0.1%
0.1618122977 1
 
< 0.1%
ValueCountFrequency (%)
0.7168458781 1
 
< 0.1%
0.6659707724 1
 
< 0.1%
0.5917159763 1
 
< 0.1%
0.5586592179 2
 
< 0.1%
0.5291005291 2
 
< 0.1%
0.5050505051 1
 
< 0.1%
0.5025125628 9
0.1%
0.5 4
< 0.1%
0.4566210046 1
 
< 0.1%
0.4366812227 7
< 0.1%

display_1
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
14154 
1
 
1053

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14154
93.1%
1 1053
 
6.9%

Length

2023-05-23T14:21:47.736988image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T14:21:47.837230image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14154
93.1%
1 1053
 
6.9%

Most occurring characters

ValueCountFrequency (%)
0 14154
93.1%
1 1053
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14154
93.1%
1 1053
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14154
93.1%
1 1053
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14154
93.1%
1 1053
 
6.9%

display_2
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
14975 
1
 
232

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14975
98.5%
1 232
 
1.5%

Length

2023-05-23T14:21:47.909274image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T14:21:48.019132image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14975
98.5%
1 232
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 14975
98.5%
1 232
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14975
98.5%
1 232
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14975
98.5%
1 232
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14975
98.5%
1 232
 
1.5%

display_3
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
14936 
1
 
271

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14936
98.2%
1 271
 
1.8%

Length

2023-05-23T14:21:48.113274image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T14:21:48.254661image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14936
98.2%
1 271
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 14936
98.2%
1 271
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14936
98.2%
1 271
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14936
98.2%
1 271
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14936
98.2%
1 271
 
1.8%

display_4
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
15117 
1
 
90

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15117
99.4%
1 90
 
0.6%

Length

2023-05-23T14:21:48.348820image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T14:21:48.489922image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15117
99.4%
1 90
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 15117
99.4%
1 90
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15117
99.4%
1 90
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15117
99.4%
1 90
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15117
99.4%
1 90
 
0.6%

display_5
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
14855 
1
 
352

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14855
97.7%
1 352
 
2.3%

Length

2023-05-23T14:21:48.599669image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T14:21:48.693798image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14855
97.7%
1 352
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 14855
97.7%
1 352
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14855
97.7%
1 352
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14855
97.7%
1 352
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14855
97.7%
1 352
 
2.3%

display_6
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
15120 
1
 
87

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15120
99.4%
1 87
 
0.6%

Length

2023-05-23T14:21:48.772349image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T14:21:48.882210image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15120
99.4%
1 87
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 15120
99.4%
1 87
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15120
99.4%
1 87
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15120
99.4%
1 87
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15120
99.4%
1 87
 
0.6%

display_7
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
14685 
1
 
522

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14685
96.6%
1 522
 
3.4%

Length

2023-05-23T14:21:48.960790image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T14:21:49.070560image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14685
96.6%
1 522
 
3.4%

Most occurring characters

ValueCountFrequency (%)
0 14685
96.6%
1 522
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14685
96.6%
1 522
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14685
96.6%
1 522
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14685
96.6%
1 522
 
3.4%

display_9
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
14948 
1
 
259

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14948
98.3%
1 259
 
1.7%

Length

2023-05-23T14:21:49.180310image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T14:21:49.321307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14948
98.3%
1 259
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 14948
98.3%
1 259
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14948
98.3%
1 259
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14948
98.3%
1 259
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14948
98.3%
1 259
 
1.7%

display_A
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
15112 
1
 
95

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15112
99.4%
1 95
 
0.6%

Length

2023-05-23T14:21:49.440626image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T14:21:49.525318image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15112
99.4%
1 95
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 15112
99.4%
1 95
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15112
99.4%
1 95
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15112
99.4%
1 95
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15112
99.4%
1 95
 
0.6%

mailer_A
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
13509 
1
1698 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13509
88.8%
1 1698
 
11.2%

Length

2023-05-23T14:21:49.932919image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T14:21:50.027163image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 13509
88.8%
1 1698
 
11.2%

Most occurring characters

ValueCountFrequency (%)
0 13509
88.8%
1 1698
 
11.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13509
88.8%
1 1698
 
11.2%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13509
88.8%
1 1698
 
11.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13509
88.8%
1 1698
 
11.2%

mailer_C
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
15205 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15205
> 99.9%
1 2
 
< 0.1%

Length

2023-05-23T14:21:50.279667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T14:21:50.406821image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15205
> 99.9%
1 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 15205
> 99.9%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15205
> 99.9%
1 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15205
> 99.9%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15205
> 99.9%
1 2
 
< 0.1%

mailer_D
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
15089 
1
 
118

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15089
99.2%
1 118
 
0.8%

Length

2023-05-23T14:21:50.485535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T14:21:50.579626image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15089
99.2%
1 118
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 15089
99.2%
1 118
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15089
99.2%
1 118
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15089
99.2%
1 118
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15089
99.2%
1 118
 
0.8%

mailer_F
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
15205 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15205
> 99.9%
1 2
 
< 0.1%

Length

2023-05-23T14:21:50.658216image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T14:21:50.783659image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15205
> 99.9%
1 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 15205
> 99.9%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15205
> 99.9%
1 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15205
> 99.9%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15205
> 99.9%
1 2
 
< 0.1%

mailer_H
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
14878 
1
 
329

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14878
97.8%
1 329
 
2.2%

Length

2023-05-23T14:21:50.862144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T14:21:50.971905image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14878
97.8%
1 329
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0 14878
97.8%
1 329
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14878
97.8%
1 329
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14878
97.8%
1 329
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14878
97.8%
1 329
 
2.2%

mailer_J
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
15158 
1
 
49

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15158
99.7%
1 49
 
0.3%

Length

2023-05-23T14:21:51.066080image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T14:21:51.160252image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15158
99.7%
1 49
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 15158
99.7%
1 49
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15158
99.7%
1 49
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15158
99.7%
1 49
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15158
99.7%
1 49
 
0.3%

mailer_L
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
15205 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15205
> 99.9%
1 2
 
< 0.1%

Length

2023-05-23T14:21:51.243414image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T14:21:51.348591image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15205
> 99.9%
1 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 15205
> 99.9%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15205
> 99.9%
1 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15205
> 99.9%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15205
> 99.9%
1 2
 
< 0.1%

marital_status_A
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
8551 
1
6656 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 8551
56.2%
1 6656
43.8%

Length

2023-05-23T14:21:51.426704image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T14:21:51.520837image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 8551
56.2%
1 6656
43.8%

Most occurring characters

ValueCountFrequency (%)
0 8551
56.2%
1 6656
43.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8551
56.2%
1 6656
43.8%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8551
56.2%
1 6656
43.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8551
56.2%
1 6656
43.8%

marital_status_B
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
13106 
1
2101 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13106
86.2%
1 2101
 
13.8%

Length

2023-05-23T14:21:51.615094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T14:21:51.709312image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 13106
86.2%
1 2101
 
13.8%

Most occurring characters

ValueCountFrequency (%)
0 13106
86.2%
1 2101
 
13.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13106
86.2%
1 2101
 
13.8%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13106
86.2%
1 2101
 
13.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13106
86.2%
1 2101
 
13.8%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
1
9675 
0
5532 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 9675
63.6%
0 5532
36.4%

Length

2023-05-23T14:21:51.787870image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T14:21:51.897663image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 9675
63.6%
0 5532
36.4%

Most occurring characters

ValueCountFrequency (%)
1 9675
63.6%
0 5532
36.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 9675
63.6%
0 5532
36.4%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 9675
63.6%
0 5532
36.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 9675
63.6%
0 5532
36.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
14946 
1
 
261

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14946
98.3%
1 261
 
1.7%

Length

2023-05-23T14:21:51.976126image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T14:21:52.070279image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14946
98.3%
1 261
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 14946
98.3%
1 261
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14946
98.3%
1 261
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14946
98.3%
1 261
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14946
98.3%
1 261
 
1.7%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
15033 
1
 
174

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15033
98.9%
1 174
 
1.1%

Length

2023-05-23T14:21:52.180103image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T14:21:52.289871image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15033
98.9%
1 174
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 15033
98.9%
1 174
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15033
98.9%
1 174
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15033
98.9%
1 174
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15033
98.9%
1 174
 
1.1%

homeowner_Renter
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
14177 
1
 
1030

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14177
93.2%
1 1030
 
6.8%

Length

2023-05-23T14:21:52.399702image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T14:21:52.509573image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14177
93.2%
1 1030
 
6.8%

Most occurring characters

ValueCountFrequency (%)
0 14177
93.2%
1 1030
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14177
93.2%
1 1030
 
6.8%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14177
93.2%
1 1030
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14177
93.2%
1 1030
 
6.8%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
14191 
1
 
1016

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14191
93.3%
1 1016
 
6.7%

Length

2023-05-23T14:21:52.588124image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T14:21:52.697926image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14191
93.3%
1 1016
 
6.7%

Most occurring characters

ValueCountFrequency (%)
0 14191
93.3%
1 1016
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14191
93.3%
1 1016
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14191
93.3%
1 1016
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14191
93.3%
1 1016
 
6.7%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
10766 
1
4441 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10766
70.8%
1 4441
29.2%

Length

2023-05-23T14:21:52.776479image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T14:21:52.871576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 10766
70.8%
1 4441
29.2%

Most occurring characters

ValueCountFrequency (%)
0 10766
70.8%
1 4441
29.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10766
70.8%
1 4441
29.2%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10766
70.8%
1 4441
29.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10766
70.8%
1 4441
29.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
10825 
1
4382 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 10825
71.2%
1 4382
28.8%

Length

2023-05-23T14:21:52.966410image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T14:21:53.060538image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 10825
71.2%
1 4382
28.8%

Most occurring characters

ValueCountFrequency (%)
0 10825
71.2%
1 4382
28.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10825
71.2%
1 4382
28.8%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10825
71.2%
1 4382
28.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10825
71.2%
1 4382
28.8%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
12825 
1
2382 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 12825
84.3%
1 2382
 
15.7%

Length

2023-05-23T14:21:53.154722image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T14:21:53.248993image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 12825
84.3%
1 2382
 
15.7%

Most occurring characters

ValueCountFrequency (%)
0 12825
84.3%
1 2382
 
15.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12825
84.3%
1 2382
 
15.7%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12825
84.3%
1 2382
 
15.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12825
84.3%
1 2382
 
15.7%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
13781 
1
1426 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13781
90.6%
1 1426
 
9.4%

Length

2023-05-23T14:21:53.348237image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T14:21:53.452922image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 13781
90.6%
1 1426
 
9.4%

Most occurring characters

ValueCountFrequency (%)
0 13781
90.6%
1 1426
 
9.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13781
90.6%
1 1426
 
9.4%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13781
90.6%
1 1426
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13781
90.6%
1 1426
 
9.4%

kid_category_1
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
12976 
1
2231 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 12976
85.3%
1 2231
 
14.7%

Length

2023-05-23T14:21:53.562708image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T14:21:53.688138image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 12976
85.3%
1 2231
 
14.7%

Most occurring characters

ValueCountFrequency (%)
0 12976
85.3%
1 2231
 
14.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12976
85.3%
1 2231
 
14.7%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12976
85.3%
1 2231
 
14.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12976
85.3%
1 2231
 
14.7%

kid_category_2
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
13612 
1
1595 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13612
89.5%
1 1595
 
10.5%

Length

2023-05-23T14:21:53.814422image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T14:21:53.957017image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 13612
89.5%
1 1595
 
10.5%

Most occurring characters

ValueCountFrequency (%)
0 13612
89.5%
1 1595
 
10.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13612
89.5%
1 1595
 
10.5%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13612
89.5%
1 1595
 
10.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13612
89.5%
1 1595
 
10.5%

kid_category_3+
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
13474 
1
1733 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13474
88.6%
1 1733
 
11.4%

Length

2023-05-23T14:21:54.067181image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T14:21:54.176941image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 13474
88.6%
1 1733
 
11.4%

Most occurring characters

ValueCountFrequency (%)
0 13474
88.6%
1 1733
 
11.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13474
88.6%
1 1733
 
11.4%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13474
88.6%
1 1733
 
11.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13474
88.6%
1 1733
 
11.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
1
9648 
0
5559 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 9648
63.4%
0 5559
36.6%

Length

2023-05-23T14:21:54.271188image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T14:21:54.365276image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 9648
63.4%
0 5559
36.6%

Most occurring characters

ValueCountFrequency (%)
1 9648
63.4%
0 5559
36.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 9648
63.4%
0 5559
36.6%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 9648
63.4%
0 5559
36.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 9648
63.4%
0 5559
36.6%

age_19-24
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
14478 
1
 
729

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14478
95.2%
1 729
 
4.8%

Length

2023-05-23T14:21:54.459415image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T14:21:54.569303image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14478
95.2%
1 729
 
4.8%

Most occurring characters

ValueCountFrequency (%)
0 14478
95.2%
1 729
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14478
95.2%
1 729
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14478
95.2%
1 729
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14478
95.2%
1 729
 
4.8%

age_25-34
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
12878 
1
2329 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 12878
84.7%
1 2329
 
15.3%

Length

2023-05-23T14:21:54.650873image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T14:21:54.757563image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 12878
84.7%
1 2329
 
15.3%

Most occurring characters

ValueCountFrequency (%)
0 12878
84.7%
1 2329
 
15.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12878
84.7%
1 2329
 
15.3%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12878
84.7%
1 2329
 
15.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12878
84.7%
1 2329
 
15.3%

age_35-44
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
11104 
1
4103 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 11104
73.0%
1 4103
 
27.0%

Length

2023-05-23T14:21:54.851193image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T14:21:54.960951image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 11104
73.0%
1 4103
 
27.0%

Most occurring characters

ValueCountFrequency (%)
0 11104
73.0%
1 4103
 
27.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11104
73.0%
1 4103
 
27.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11104
73.0%
1 4103
 
27.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11104
73.0%
1 4103
 
27.0%

age_45-54
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
9016 
1
6191 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 9016
59.3%
1 6191
40.7%

Length

2023-05-23T14:21:55.055083image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T14:21:55.180499image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 9016
59.3%
1 6191
40.7%

Most occurring characters

ValueCountFrequency (%)
0 9016
59.3%
1 6191
40.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9016
59.3%
1 6191
40.7%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9016
59.3%
1 6191
40.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9016
59.3%
1 6191
40.7%

age_55-64
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
14385 
1
 
822

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14385
94.6%
1 822
 
5.4%

Length

2023-05-23T14:21:55.290292image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T14:21:55.384444image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14385
94.6%
1 822
 
5.4%

Most occurring characters

ValueCountFrequency (%)
0 14385
94.6%
1 822
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14385
94.6%
1 822
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14385
94.6%
1 822
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14385
94.6%
1 822
 
5.4%

age_65+
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
14174 
1
 
1033

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 14174
93.2%
1 1033
 
6.8%

Length

2023-05-23T14:21:55.478621image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T14:21:55.572752image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14174
93.2%
1 1033
 
6.8%

Most occurring characters

ValueCountFrequency (%)
0 14174
93.2%
1 1033
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14174
93.2%
1 1033
 
6.8%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14174
93.2%
1 1033
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14174
93.2%
1 1033
 
6.8%

income_100-124K
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
14582 
1
 
625

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14582
95.9%
1 625
 
4.1%

Length

2023-05-23T14:21:55.666922image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T14:21:55.792333image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14582
95.9%
1 625
 
4.1%

Most occurring characters

ValueCountFrequency (%)
0 14582
95.9%
1 625
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14582
95.9%
1 625
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14582
95.9%
1 625
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14582
95.9%
1 625
 
4.1%

income_125-149K
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
14294 
1
 
913

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14294
94.0%
1 913
 
6.0%

Length

2023-05-23T14:21:55.886595image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T14:21:55.980825image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14294
94.0%
1 913
 
6.0%

Most occurring characters

ValueCountFrequency (%)
0 14294
94.0%
1 913
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14294
94.0%
1 913
 
6.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14294
94.0%
1 913
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14294
94.0%
1 913
 
6.0%

income_15-24K
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
14031 
1
 
1176

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14031
92.3%
1 1176
 
7.7%

Length

2023-05-23T14:21:56.059422image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T14:21:56.154216image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14031
92.3%
1 1176
 
7.7%

Most occurring characters

ValueCountFrequency (%)
0 14031
92.3%
1 1176
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14031
92.3%
1 1176
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14031
92.3%
1 1176
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14031
92.3%
1 1176
 
7.7%

income_150-174K
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
14586 
1
 
621

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14586
95.9%
1 621
 
4.1%

Length

2023-05-23T14:21:56.247346image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T14:21:56.341531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14586
95.9%
1 621
 
4.1%

Most occurring characters

ValueCountFrequency (%)
0 14586
95.9%
1 621
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14586
95.9%
1 621
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14586
95.9%
1 621
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14586
95.9%
1 621
 
4.1%

income_175-199K
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
14947 
1
 
260

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14947
98.3%
1 260
 
1.7%

Length

2023-05-23T14:21:56.420088image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T14:21:56.514268image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14947
98.3%
1 260
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 14947
98.3%
1 260
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14947
98.3%
1 260
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14947
98.3%
1 260
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14947
98.3%
1 260
 
1.7%

income_200-249K
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
15149 
1
 
58

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15149
99.6%
1 58
 
0.4%

Length

2023-05-23T14:21:56.592829image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T14:21:57.110268image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15149
99.6%
1 58
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 15149
99.6%
1 58
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15149
99.6%
1 58
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15149
99.6%
1 58
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15149
99.6%
1 58
 
0.4%

income_25-34K
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
13687 
1
1520 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13687
90.0%
1 1520
 
10.0%

Length

2023-05-23T14:21:57.188854image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T14:21:57.298648image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 13687
90.0%
1 1520
 
10.0%

Most occurring characters

ValueCountFrequency (%)
0 13687
90.0%
1 1520
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13687
90.0%
1 1520
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13687
90.0%
1 1520
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13687
90.0%
1 1520
 
10.0%

income_250K+
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
14838 
1
 
369

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14838
97.6%
1 369
 
2.4%

Length

2023-05-23T14:21:57.377152image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T14:21:57.486900image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14838
97.6%
1 369
 
2.4%

Most occurring characters

ValueCountFrequency (%)
0 14838
97.6%
1 369
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14838
97.6%
1 369
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14838
97.6%
1 369
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14838
97.6%
1 369
 
2.4%

income_35-49K
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
12300 
1
2907 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 12300
80.9%
1 2907
 
19.1%

Length

2023-05-23T14:21:57.581091image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T14:21:57.769366image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 12300
80.9%
1 2907
 
19.1%

Most occurring characters

ValueCountFrequency (%)
0 12300
80.9%
1 2907
 
19.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12300
80.9%
1 2907
 
19.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12300
80.9%
1 2907
 
19.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12300
80.9%
1 2907
 
19.1%

income_50-74K
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
11470 
1
3737 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 11470
75.4%
1 3737
 
24.6%

Length

2023-05-23T14:21:57.894786image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T14:21:58.035783image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 11470
75.4%
1 3737
 
24.6%

Most occurring characters

ValueCountFrequency (%)
0 11470
75.4%
1 3737
 
24.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11470
75.4%
1 3737
 
24.6%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11470
75.4%
1 3737
 
24.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11470
75.4%
1 3737
 
24.6%

income_75-99K
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
13332 
1
1875 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13332
87.7%
1 1875
 
12.3%

Length

2023-05-23T14:21:58.161572image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T14:21:58.318208image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 13332
87.7%
1 1875
 
12.3%

Most occurring characters

ValueCountFrequency (%)
0 13332
87.7%
1 1875
 
12.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13332
87.7%
1 1875
 
12.3%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13332
87.7%
1 1875
 
12.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13332
87.7%
1 1875
 
12.3%

income_Under 15K
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
14061 
1
 
1146

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14061
92.5%
1 1146
 
7.5%

Length

2023-05-23T14:21:58.459223image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T14:21:58.600280image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14061
92.5%
1 1146
 
7.5%

Most occurring characters

ValueCountFrequency (%)
0 14061
92.5%
1 1146
 
7.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14061
92.5%
1 1146
 
7.5%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14061
92.5%
1 1146
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14061
92.5%
1 1146
 
7.5%

hhsize_1
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
10604 
1
4603 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10604
69.7%
1 4603
30.3%

Length

2023-05-23T14:21:58.710106image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T14:21:58.860298image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 10604
69.7%
1 4603
30.3%

Most occurring characters

ValueCountFrequency (%)
0 10604
69.7%
1 4603
30.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10604
69.7%
1 4603
30.3%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10604
69.7%
1 4603
30.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10604
69.7%
1 4603
30.3%

hhsize_2
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
9925 
1
5282 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 9925
65.3%
1 5282
34.7%

Length

2023-05-23T14:21:59.008865image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T14:21:59.165895image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 9925
65.3%
1 5282
34.7%

Most occurring characters

ValueCountFrequency (%)
0 9925
65.3%
1 5282
34.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9925
65.3%
1 5282
34.7%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9925
65.3%
1 5282
34.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9925
65.3%
1 5282
34.7%

hhsize_3
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
12846 
1
2361 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 12846
84.5%
1 2361
 
15.5%

Length

2023-05-23T14:21:59.291282image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T14:21:59.447957image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 12846
84.5%
1 2361
 
15.5%

Most occurring characters

ValueCountFrequency (%)
0 12846
84.5%
1 2361
 
15.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12846
84.5%
1 2361
 
15.5%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12846
84.5%
1 2361
 
15.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12846
84.5%
1 2361
 
15.5%

hhsize_4
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
13834 
1
 
1373

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13834
91.0%
1 1373
 
9.0%

Length

2023-05-23T14:21:59.573813image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T14:21:59.730516image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 13834
91.0%
1 1373
 
9.0%

Most occurring characters

ValueCountFrequency (%)
0 13834
91.0%
1 1373
 
9.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13834
91.0%
1 1373
 
9.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13834
91.0%
1 1373
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13834
91.0%
1 1373
 
9.0%

hhsize_5+
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
13619 
1
1588 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13619
89.6%
1 1588
 
10.4%

Length

2023-05-23T14:21:59.862403image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T14:21:59.997323image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 13619
89.6%
1 1588
 
10.4%

Most occurring characters

ValueCountFrequency (%)
0 13619
89.6%
1 1588
 
10.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13619
89.6%
1 1588
 
10.4%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13619
89.6%
1 1588
 
10.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13619
89.6%
1 1588
 
10.4%

campaign_8.0
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
15180 
1
 
27

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15180
99.8%
1 27
 
0.2%

Length

2023-05-23T14:22:00.138355image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T14:22:00.279836image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15180
99.8%
1 27
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 15180
99.8%
1 27
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15180
99.8%
1 27
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15180
99.8%
1 27
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15180
99.8%
1 27
 
0.2%

campaign_13.0
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
15178 
1
 
29

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15178
99.8%
1 29
 
0.2%

Length

2023-05-23T14:22:00.420837image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T14:22:00.563883image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15178
99.8%
1 29
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 15178
99.8%
1 29
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15178
99.8%
1 29
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15178
99.8%
1 29
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15178
99.8%
1 29
 
0.2%

campaign_18.0
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
15162 
1
 
45

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15162
99.7%
1 45
 
0.3%

Length

2023-05-23T14:22:00.687699image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T14:22:00.860029image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15162
99.7%
1 45
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 15162
99.7%
1 45
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15162
99.7%
1 45
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15162
99.7%
1 45
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15162
99.7%
1 45
 
0.3%

campaign_25.0
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
15206 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15206
> 99.9%
1 1
 
< 0.1%

Length

2023-05-23T14:22:00.985821image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T14:22:01.111201image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15206
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 15206
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15206
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15206
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15206
> 99.9%
1 1
 
< 0.1%

campaign_26.0
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
15201 
1
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15201
> 99.9%
1 6
 
< 0.1%

Length

2023-05-23T14:22:01.252265image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T14:22:01.409353image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15201
> 99.9%
1 6
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 15201
> 99.9%
1 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15201
> 99.9%
1 6
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15201
> 99.9%
1 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15201
> 99.9%
1 6
 
< 0.1%

campaign_30.0
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
15206 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15206
> 99.9%
1 1
 
< 0.1%

Length

2023-05-23T14:22:01.519153image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T14:22:01.666165image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15206
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 15206
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15206
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15206
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15206
> 99.9%
1 1
 
< 0.1%

description_TypeA
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
15099 
1
 
108

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15099
99.3%
1 108
 
0.7%

Length

2023-05-23T14:22:01.785832image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T14:22:01.942423image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15099
99.3%
1 108
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 15099
99.3%
1 108
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15099
99.3%
1 108
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15099
99.3%
1 108
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15099
99.3%
1 108
 
0.7%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.9 KiB
0
15206 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15207
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15206
> 99.9%
1 1
 
< 0.1%

Length

2023-05-23T14:22:02.066727image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-23T14:22:02.209262image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 15206
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 15206
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15207
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15206
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15207
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15206
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15206
> 99.9%
1 1
 
< 0.1%

product_size
Categorical

HIGH CARDINALITY  MISSING 

Distinct319
Distinct (%)2.4%
Missing2019
Missing (%)13.3%
Memory size118.9 KiB
12
 
866
4.4
 
749
4.25
 
591
14
 
586
11
 
571
Other values (314)
9825 

Length

Max length10
Median length9
Mean length3.0258568
Min length1

Characters and Unicode

Total characters39905
Distinct characters32
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique53 ?
Unique (%)0.4%

Sample

1st row18
2nd row6
3rd row6
4th row3.5
5th row3.5

Common Values

ValueCountFrequency (%)
12 866
 
5.7%
4.4 749
 
4.9%
4.25 591
 
3.9%
14 586
 
3.9%
11 571
 
3.8%
13 504
 
3.3%
4.5 446
 
2.9%
16 436
 
2.9%
5 269
 
1.8%
7 262
 
1.7%
Other values (309) 7908
52.0%
(Missing) 2019
 
13.3%

Length

2023-05-23T14:22:02.319014image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
12 1285
 
8.9%
4.4 749
 
5.2%
14 653
 
4.5%
4.25 591
 
4.1%
11 586
 
4.0%
13 535
 
3.7%
pk 528
 
3.6%
6 457
 
3.2%
4.5 447
 
3.1%
16 438
 
3.0%
Other values (272) 8238
56.8%

Most occurring characters

ValueCountFrequency (%)
1 7493
18.8%
. 6511
16.3%
4 4586
11.5%
2 4263
10.7%
5 3893
9.8%
3 2036
 
5.1%
6 2009
 
5.0%
1319
 
3.3%
7 1312
 
3.3%
8 1206
 
3.0%
Other values (22) 5277
13.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28513
71.5%
Other Punctuation 6605
 
16.6%
Uppercase Letter 3468
 
8.7%
Space Separator 1319
 
3.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 794
22.9%
C 670
19.3%
K 633
18.3%
T 359
10.4%
O 260
 
7.5%
U 183
 
5.3%
N 163
 
4.7%
E 153
 
4.4%
Z 95
 
2.7%
B 44
 
1.3%
Other values (9) 114
 
3.3%
Decimal Number
ValueCountFrequency (%)
1 7493
26.3%
4 4586
16.1%
2 4263
15.0%
5 3893
13.7%
3 2036
 
7.1%
6 2009
 
7.0%
7 1312
 
4.6%
8 1206
 
4.2%
0 1086
 
3.8%
9 629
 
2.2%
Other Punctuation
ValueCountFrequency (%)
. 6511
98.6%
/ 94
 
1.4%
Space Separator
ValueCountFrequency (%)
1319
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 36437
91.3%
Latin 3468
 
8.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 794
22.9%
C 670
19.3%
K 633
18.3%
T 359
10.4%
O 260
 
7.5%
U 183
 
5.3%
N 163
 
4.7%
E 153
 
4.4%
Z 95
 
2.7%
B 44
 
1.3%
Other values (9) 114
 
3.3%
Common
ValueCountFrequency (%)
1 7493
20.6%
. 6511
17.9%
4 4586
12.6%
2 4263
11.7%
5 3893
10.7%
3 2036
 
5.6%
6 2009
 
5.5%
1319
 
3.6%
7 1312
 
3.6%
8 1206
 
3.3%
Other values (3) 1809
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 39905
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 7493
18.8%
. 6511
16.3%
4 4586
11.5%
2 4263
10.7%
5 3893
9.8%
3 2036
 
5.1%
6 2009
 
5.0%
1319
 
3.3%
7 1312
 
3.3%
8 1206
 
3.0%
Other values (22) 5277
13.2%

Interactions

2023-05-23T14:21:41.928613image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T14:21:39.308859image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T14:21:39.922597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T14:21:40.523839image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T14:21:41.081246image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T14:21:42.069718image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T14:21:39.450320image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T14:21:40.046368image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T14:21:40.627019image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T14:21:41.191086image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T14:21:42.242354image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T14:21:39.560104image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T14:21:40.156188image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T14:21:40.752396image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T14:21:41.316507image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T14:21:42.398985image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T14:21:39.685447image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T14:21:40.281597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T14:21:40.846537image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T14:21:41.410673image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T14:21:42.540393image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T14:21:39.795211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T14:21:40.391353image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T14:21:40.971442image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-23T14:21:41.771599image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-05-23T14:22:02.586169image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Unnamed: 0shelf_pricepct_discpct_retail_discpct_coupon_discfirst_purchasedisplay_1display_2display_3display_4display_5display_6display_7display_9display_Amailer_Amailer_Cmailer_Dmailer_Fmailer_Hmailer_Jmailer_Lmarital_status_Amarital_status_Bhomeowner_Homeownerhomeowner_Probable Ownerhomeowner_Probable Renterhomeowner_Renterhhcomp_1 Adult Kidshhcomp_2 Adults Kidshhcomp_2 Adults No Kidshhcomp_Single Femalehhcomp_Single Malekid_category_1kid_category_2kid_category_3+kid_category_None/Unknownage_19-24age_25-34age_35-44age_45-54age_55-64age_65+income_100-124Kincome_125-149Kincome_15-24Kincome_150-174Kincome_175-199Kincome_200-249Kincome_25-34Kincome_250K+income_35-49Kincome_50-74Kincome_75-99Kincome_Under 15Khhsize_1hhsize_2hhsize_3hhsize_4hhsize_5+campaign_8.0campaign_13.0campaign_18.0campaign_25.0campaign_26.0campaign_30.0description_TypeAdescription_TypeB
Unnamed: 01.000-0.0170.0490.0490.0040.0490.0380.0310.0230.0280.0340.0190.0260.0120.0440.0360.0280.0000.0000.0160.0270.0000.1140.1480.1670.2030.1400.1750.1440.0760.1060.0810.2110.1310.1470.1750.1000.1350.1080.1040.1380.1080.1660.1320.2040.1810.1180.1490.1690.1310.1520.1220.1790.1490.1380.1190.1280.1520.2230.1320.0170.0000.0240.0060.0000.0010.0200.006
shelf_price-0.0171.000-0.001-0.0080.0640.0550.0000.0090.0050.0000.0000.0000.0000.0000.0000.0040.0000.0000.0000.0000.0000.0000.0260.0000.0280.0080.0000.0000.0000.0100.0260.0200.0140.0000.0000.0180.0050.0000.0000.0000.0130.0360.0000.0000.0000.0240.0390.0000.0200.0120.0000.0000.0000.0280.0000.0040.0200.0000.0000.0190.0000.1850.0000.0000.0000.0000.0940.000
pct_disc0.049-0.0011.0000.9880.1240.1500.2180.1070.0870.0680.0780.0300.0910.0670.0110.4090.0000.1430.0000.1830.0790.0450.0480.0280.0700.0230.0000.0550.0380.0280.0540.0160.0460.0000.0260.0110.0410.0380.0440.0380.0190.0170.0390.0450.0000.0670.0500.0440.0180.0430.0430.0440.0240.0360.0480.0390.0570.0220.0500.0140.0630.0500.0380.0170.0380.0000.0320.017
pct_retail_disc0.049-0.0080.9881.000-0.0150.1520.2210.1080.0890.0770.0800.0310.0910.0680.0110.4130.0000.1430.0000.1870.0800.0460.0520.0280.0750.0210.0000.0560.0420.0310.0530.0170.0510.0120.0300.0130.0420.0380.0450.0360.0230.0190.0400.0440.0000.0680.0520.0430.0180.0410.0420.0470.0260.0410.0490.0400.0540.0270.0490.0170.0640.0190.0320.0170.0460.0000.0360.017
pct_coupon_disc0.0040.0640.124-0.0151.0000.0260.0000.0000.0000.0240.0000.0000.0210.0000.0000.0000.0000.0000.0000.0330.0000.0000.0140.0300.0240.0000.0000.0140.0000.0150.0000.0000.0240.0110.0000.0200.0000.0000.0180.0220.0070.0000.0170.0120.0350.0000.0000.0000.0000.0000.0000.0000.0000.0560.0000.0000.0000.0090.0000.0250.0000.2050.2150.0000.0700.0000.1860.000
first_purchase0.0490.0550.1500.1520.0261.0000.0100.0120.0130.0120.0170.0000.0000.0120.0090.0320.0000.0000.0000.0000.0180.0000.0220.0120.0040.0180.0150.0210.0070.0320.0060.0340.0160.0320.0000.0080.0360.0000.0300.0230.0000.0050.0630.0000.0270.0100.0410.0260.0250.0350.0000.0110.0120.0160.0000.0420.0060.0240.0120.0310.0140.0150.0330.0000.0040.0000.0380.000
display_10.0380.0000.2180.2210.0000.0101.0000.0320.0350.0180.0400.0170.0500.0340.0180.1520.0000.0710.0000.0810.0220.0000.0000.0000.0090.0080.0020.0080.0190.0000.0080.0000.0010.0000.0100.0000.0080.0000.0000.0000.0000.0000.0000.0270.0050.0280.0110.0240.0000.0280.0220.0000.0150.0350.0240.0000.0000.0000.0080.0000.0000.0040.0090.0000.0000.0000.0090.000
display_20.0310.0090.1070.1080.0000.0120.0321.0000.0120.0000.0150.0000.0200.0120.0000.0700.0000.0140.0000.0380.0000.0000.0150.0140.0040.0000.0000.0000.0290.0000.0110.0000.0000.0080.0200.0150.0070.0000.0210.0120.0000.0050.0000.0140.0000.0000.0170.0000.0000.0000.0000.0180.0000.0020.0090.0000.0000.0000.0000.0110.0110.0000.0000.0000.0000.0000.0000.000
display_30.0230.0050.0870.0890.0000.0130.0350.0121.0000.0000.0170.0000.0230.0140.0000.0570.0000.0000.0000.0000.0000.0000.0130.0000.0010.0000.0000.0130.0000.0090.0000.0070.0110.0140.0000.0000.0090.0000.0000.0000.0090.0150.0000.0000.0210.0000.0000.0010.0000.0000.0000.0120.0000.0000.0040.0000.0090.0130.0030.0000.0000.0000.0000.0000.0060.0000.0000.000
display_40.0280.0000.0680.0770.0240.0120.0180.0000.0001.0000.0040.0000.0090.0000.0000.0270.0000.0000.0000.0000.0000.0000.0190.0000.0130.0000.0000.0080.0130.0040.0000.0000.0030.0000.0000.0060.0130.0000.0000.0000.0020.0000.0090.0050.0000.0160.0000.0000.0000.0230.0000.0230.0000.0050.0070.0130.0000.0000.0000.0010.0000.0000.0000.0000.0000.0000.0000.000
display_50.0340.0000.0780.0800.0000.0170.0400.0150.0170.0041.0000.0030.0270.0170.0050.0330.0000.0000.0000.0000.0000.0000.0200.0030.0370.0160.0060.0130.0380.0000.0130.0000.0020.0080.0180.0120.0110.0290.0000.0130.0050.0100.0080.0160.0000.0090.0070.0000.0000.0050.0080.0000.0000.0000.0160.0000.0120.0000.0210.0000.0180.0000.0000.0000.0000.0240.0000.000
display_60.0190.0000.0300.0310.0000.0000.0170.0000.0000.0000.0031.0000.0090.0000.0000.0260.0000.0000.0000.0000.0800.0000.0000.0000.0000.0000.0000.0020.0080.0000.0000.0000.0000.0160.0000.0000.0050.0000.0040.0000.0140.0000.0000.0110.0000.0000.0000.0000.0000.0000.0000.0000.0020.0060.0150.0000.0000.0170.0000.0000.0270.0000.0000.0000.0000.0000.0040.000
display_70.0260.0000.0910.0910.0210.0000.0500.0200.0230.0090.0270.0091.0000.0220.0100.0760.0000.0000.0000.0000.0000.0000.0000.0000.0130.0130.0240.0370.0000.0000.0200.0000.0000.0000.0000.0000.0000.0000.0090.0090.0000.0000.0000.0000.0110.0000.0070.0000.0000.0030.0000.0120.0000.0100.0000.0150.0140.0000.0000.0000.0820.0000.0000.0000.0000.0000.0280.000
display_90.0120.0000.0670.0680.0000.0120.0340.0120.0140.0000.0170.0000.0221.0000.0000.0450.0000.0000.0000.0000.0000.0000.0230.0000.0000.0000.0000.0000.0000.0170.0000.0000.0000.0070.0000.0000.0130.0050.0100.0030.0000.0000.0090.0000.0000.0090.0000.0000.0000.0000.0000.0120.0000.0080.0000.0000.0060.0060.0000.0000.0100.0000.0000.0000.0000.0000.0000.000
display_A0.0440.0000.0110.0110.0000.0090.0180.0000.0000.0000.0050.0000.0100.0001.0000.0000.0000.0000.0000.0020.0160.0000.0000.0000.0170.0000.0000.0000.0000.0140.0220.0000.0320.0000.0360.0000.0080.0090.0000.0000.0000.0180.0100.0000.0160.0060.0000.0000.0000.0000.0050.0080.0210.0310.0000.0110.0280.0000.0310.0000.0000.0000.0000.0000.0000.0000.0000.000
mailer_A0.0360.0040.4090.4130.0000.0320.1520.0700.0570.0270.0330.0260.0760.0450.0001.0000.0000.0290.0000.0510.0160.0000.0070.0000.0330.0030.0000.0130.0100.0150.0270.0000.0170.0140.0000.0140.0210.0130.0200.0280.0170.0000.0000.0000.0070.0370.0300.0200.0150.0120.0000.0000.0150.0280.0150.0100.0310.0000.0110.0040.0560.0240.0150.0000.0170.0000.0350.000
mailer_C0.0280.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0080.0000.0000.0000.0000.0230.0000.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0110.0000.0000.0000.0000.0000.0250.0000.0000.0000.0000.0000.0000.0000.0000.000
mailer_D0.0000.0000.1430.1430.0000.0000.0710.0140.0000.0000.0000.0000.0000.0000.0000.0290.0001.0000.0000.0070.0000.0000.0160.0130.0060.0000.0000.0070.0310.0000.0000.0000.0000.0220.0000.0000.0100.0000.0000.0080.0080.0180.0000.0000.0110.0050.0030.0000.0000.0000.0080.0000.0000.0080.0170.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
mailer_F0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0080.0000.0000.0000.0000.0000.0210.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0230.0000.0000.0000.0000.0000.0000.0000.000
mailer_H0.0160.0000.1830.1870.0330.0000.0810.0380.0000.0000.0000.0000.0000.0000.0020.0510.0000.0070.0001.0000.0000.0000.0000.0040.0000.0000.0000.0120.0000.0000.0000.0080.0100.0000.0120.0000.0000.0040.0200.0110.0270.0280.0160.0040.0000.0090.0110.0000.0000.0000.0000.0160.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.000
mailer_J0.0270.0000.0790.0800.0000.0180.0220.0000.0000.0000.0000.0800.0000.0000.0160.0160.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0060.0130.0000.0110.0060.0000.0000.0100.0000.0000.0000.0000.0000.0000.0050.0020.0040.0000.0000.0120.0000.0000.0100.0000.0290.0000.0100.0000.0170.0190.0100.0090.0000.0000.0000.0000.0000.0000.0000.0000.000
mailer_L0.0000.0000.0450.0460.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
marital_status_A0.1140.0260.0480.0520.0140.0220.0000.0150.0130.0190.0200.0000.0000.0230.0000.0070.0000.0160.0000.0000.0000.0001.0000.3530.4630.0100.0900.0430.1220.4290.1120.2310.1920.1650.0680.2740.3460.1420.0400.0920.0410.1020.0000.0830.1370.1000.0470.0150.0690.0620.1180.0460.0140.0700.1170.5810.1820.1400.1180.3120.0080.0000.0000.0000.0000.0000.0020.000
marital_status_B0.1480.0000.0280.0280.0300.0120.0000.0140.0000.0000.0030.0000.0000.0000.0000.0000.0000.0130.0000.0040.0000.0000.3531.0000.1460.0150.0000.2620.2000.2010.1230.2130.1600.0890.0060.0280.0910.1300.0420.0890.1660.0320.0220.0050.0490.0100.0320.0210.0220.1350.0540.0160.0960.0100.0900.2550.1340.0420.0190.1030.0000.0000.0000.0000.0000.0000.0000.000
homeowner_Homeowner0.1670.0280.0700.0750.0240.0040.0090.0040.0010.0130.0370.0000.0130.0000.0170.0330.0000.0060.0000.0000.0000.0000.4630.1461.0000.1740.1410.3560.1170.2560.2450.2850.1790.1000.0310.1240.1760.2120.0760.0050.0630.0680.1220.0120.1290.1160.1170.0080.0310.1440.0900.0830.0350.1520.1120.4250.1960.1150.0350.1620.0110.0000.0000.0000.0000.0000.0080.000
homeowner_Probable Owner0.2030.0080.0230.0210.0000.0180.0080.0000.0000.0000.0160.0000.0130.0000.0000.0030.0000.0000.0000.0000.0000.0000.0100.0150.1741.0000.0090.0340.0290.0170.0000.1180.0410.0000.0000.0460.0350.0170.0360.0690.0130.0290.0000.0500.0310.0360.0370.0130.0000.0000.0170.0000.0030.0410.0360.0360.0000.0060.0000.0440.0000.0000.0000.0000.0000.0000.0000.000
homeowner_Probable Renter0.1400.0000.0000.0000.0000.0150.0020.0000.0000.0000.0060.0000.0240.0000.0000.0000.0000.0000.0000.0000.0000.0000.0900.0000.1410.0091.0000.0270.0260.0680.0670.1060.0000.0430.0350.0370.0810.0000.0070.0400.0620.0000.0190.0190.0250.1130.0190.0090.0000.0800.0130.0220.0470.0390.0300.1580.0730.0450.0320.0350.0000.0000.0000.0000.0000.0000.0000.000
homeowner_Renter0.1750.0000.0550.0560.0140.0210.0080.0000.0130.0080.0130.0020.0370.0000.0000.0130.0000.0070.0000.0120.0060.0000.0430.2620.3560.0340.0271.0000.1760.0500.0960.0030.0920.0330.0470.0580.0430.2080.0540.0740.0650.0390.0660.0370.0040.0100.0540.0340.0120.0870.0410.0240.1150.0250.1290.0000.0160.0660.1660.0380.0000.0000.0090.0000.0000.0000.0030.000
hhcomp_1 Adult Kids0.1440.0000.0380.0420.0000.0070.0190.0290.0000.0130.0380.0080.0000.0000.0000.0100.0000.0310.0000.0000.0130.0000.1220.2000.1170.0290.0260.1761.0000.1710.1700.1150.0850.1020.2650.1630.3520.1280.1390.0330.1350.0430.0550.0370.0160.0000.0540.0330.0120.0310.0410.0600.0850.0960.2400.1760.0670.1920.0930.0520.0000.0030.0000.0000.0000.0000.0080.000
hhcomp_2 Adults Kids0.0760.0100.0280.0310.0150.0320.0000.0000.0090.0040.0000.0000.0000.0170.0140.0150.0080.0000.0080.0000.0000.0000.4290.2010.2560.0170.0680.0500.1711.0000.4080.2760.2060.5020.3360.3980.8460.0520.0500.1550.0600.0420.1460.0750.1430.0530.0300.0140.0450.0810.0550.0340.0190.0440.0830.4230.4680.4750.3920.4340.0040.0060.0190.0000.0000.0000.0000.000
hhcomp_2 Adults No Kids0.1060.0260.0540.0530.0000.0060.0080.0110.0000.0000.0130.0000.0200.0000.0220.0270.0000.0000.0000.0000.0110.0000.1120.1230.2450.0000.0670.0960.1700.4081.0000.2740.2040.2630.2170.2280.4830.0110.0710.0640.0410.0220.1240.0750.0370.0000.0730.0410.0040.0070.0530.0390.0040.0210.0600.4190.8720.2720.2000.2170.0000.0050.0270.0000.0000.0000.0140.000
hhcomp_Single Female0.0810.0200.0160.0170.0000.0340.0000.0000.0070.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0080.0060.0000.2310.2130.2850.1180.1060.0030.1150.2760.2741.0000.1380.1780.1470.1540.3270.0450.0570.0360.0370.0000.0550.0240.0380.0890.0380.0660.0240.1670.0290.0780.0570.0640.0470.4940.1590.1840.1350.1470.0140.0090.0000.0000.0000.0000.0000.000
hhcomp_Single Male0.2110.0140.0460.0510.0240.0160.0010.0000.0110.0030.0020.0000.0000.0000.0320.0170.0000.0000.0000.0100.0000.0000.1920.1600.1790.0410.0000.0920.0850.2060.2040.1381.0000.1330.1090.1150.2440.0000.0930.0260.0730.0630.0000.0000.0780.0380.0000.0300.0160.0080.0400.0580.0850.0670.0150.3890.1380.1370.1010.1090.0000.0000.0000.0000.0000.0080.0000.000
kid_category_10.1310.0000.0000.0120.0110.0320.0000.0080.0140.0000.0080.0160.0000.0070.0000.0140.0000.0220.0000.0000.0000.0000.1650.0890.1000.0000.0430.0330.1020.5020.2630.1780.1331.0000.1410.1480.5460.0000.0090.1500.0910.0430.0650.0220.0360.0330.0000.0370.0480.0300.0820.0240.0560.0310.0090.2730.2100.8450.1300.1410.0000.0000.0070.0000.0130.0000.0000.000
kid_category_20.1470.0000.0260.0300.0000.0000.0100.0200.0000.0000.0180.0000.0000.0000.0360.0000.0230.0000.0000.0120.0100.0000.0680.0060.0310.0000.0350.0470.2650.3360.2170.1470.1090.1411.0000.1220.4510.0690.1010.0270.0240.0130.0000.0400.0050.0370.0510.0440.0180.0410.0530.0060.0280.0190.0280.2250.2490.0700.8110.1160.0000.0000.0030.0000.0000.0000.0000.000
kid_category_3+0.1750.0180.0110.0130.0200.0080.0000.0150.0000.0060.0120.0000.0000.0000.0000.0140.0000.0000.0210.0000.0000.0000.2740.0280.1240.0460.0370.0580.1630.3980.2280.1540.1150.1480.1221.0000.4720.0850.0810.0470.0600.0660.0960.0600.1880.0880.0430.0000.0140.0220.0000.0490.0530.0210.0550.2360.2610.1530.0000.9520.0200.0030.0110.0060.0000.0000.0000.006
kid_category_None/Unknown0.1000.0050.0410.0420.0000.0360.0080.0070.0090.0130.0110.0050.0000.0130.0080.0210.0040.0100.0040.0000.0000.0000.3460.0910.1760.0350.0810.0430.3520.8460.4830.3270.2440.5460.4510.4721.0000.0110.1270.1240.1230.0660.1120.0480.1570.0590.0000.0000.0330.0650.0270.0000.0580.0000.0460.5000.4860.5650.4150.4500.0030.0000.0240.0000.0000.0000.0000.000
age_19-240.1350.0000.0380.0380.0000.0000.0000.0000.0000.0000.0290.0000.0000.0050.0090.0130.0000.0000.0000.0040.0000.0000.1420.1300.2120.0170.0000.2080.1280.0520.0110.0450.0000.0000.0690.0850.0111.0000.0950.1360.1850.0520.0590.0450.0550.0080.0450.0270.0110.0240.0390.0000.0450.0480.2700.0180.0220.0240.0720.0300.0140.0000.0000.0000.0000.0000.0000.000
age_25-340.1080.0000.0440.0450.0180.0300.0000.0210.0000.0000.0000.0040.0090.0100.0000.0200.0000.0000.0000.0200.0000.0000.0400.0420.0760.0360.0070.0540.1390.0500.0710.0570.0930.0090.1010.0810.1270.0951.0000.2580.3520.1010.1140.0600.0360.0000.0360.0260.0000.0000.0660.0290.0290.0460.0160.0540.0550.0350.0270.0980.0000.0100.0480.0000.0000.0000.0320.000
age_35-440.1040.0000.0380.0360.0220.0230.0000.0120.0000.0000.0130.0000.0090.0030.0000.0280.0000.0080.0000.0110.0000.0000.0920.0890.0050.0690.0400.0740.0330.1550.0640.0360.0260.1500.0270.0470.1240.1360.2581.0000.5040.1450.1640.0740.0440.0000.0050.0470.0300.0170.0640.0330.0000.0490.0600.0490.0750.1370.0350.0630.0000.0210.0190.0000.0110.0000.0000.000
age_45-540.1380.0130.0190.0230.0070.0000.0000.0000.0090.0020.0050.0140.0000.0000.0000.0170.0000.0080.0000.0270.0000.0000.0410.1660.0630.0130.0620.0650.1350.0600.0410.0370.0730.0910.0240.0600.1230.1850.3520.5041.0000.1980.2230.0000.0370.0250.0270.1020.0250.0190.0080.0550.0250.0430.0760.1060.0050.1210.0060.0390.0000.0240.0000.0000.0100.0000.0220.000
age_55-640.1080.0360.0170.0190.0000.0050.0000.0050.0150.0000.0100.0000.0000.0000.0180.0000.0000.0180.0000.0280.0050.0000.1020.0320.0680.0290.0000.0390.0430.0420.0220.0000.0630.0430.0130.0660.0660.0520.1010.1450.1981.0000.0630.0810.0320.0350.0760.0120.0090.0270.0360.0680.0270.0150.0200.0240.0840.0480.0280.0620.0000.0000.0060.0000.0000.0000.0000.000
age_65+0.1660.0000.0390.0400.0170.0630.0000.0000.0000.0090.0080.0000.0000.0090.0100.0000.0000.0000.0000.0160.0020.0000.0000.0220.1220.0000.0190.0660.0550.1460.1240.0550.0000.0650.0000.0960.1120.0590.1140.1640.2230.0631.0000.0000.0190.0630.0450.0340.0120.0130.0410.0690.0410.0480.0720.0340.1380.0000.0840.0910.0000.0000.0000.0000.0120.0000.0000.000
income_100-124K0.1320.0000.0450.0440.0120.0000.0270.0140.0000.0050.0160.0110.0000.0000.0000.0000.0000.0000.0000.0040.0040.0000.0830.0050.0120.0500.0190.0370.0370.0750.0750.0240.0000.0220.0400.0600.0480.0450.0600.0740.0000.0810.0001.0000.0510.0590.0410.0250.0060.0680.0310.1000.1180.0770.0580.0210.0780.0270.0540.0690.0000.0000.0060.0000.0000.0000.0000.000
income_125-149K0.2040.0000.0000.0000.0350.0270.0050.0000.0210.0000.0000.0000.0110.0000.0160.0070.0000.0110.0000.0000.0000.0000.1370.0490.1290.0310.0250.0040.0160.1430.0370.0380.0780.0360.0050.1880.1570.0550.0360.0440.0370.0320.0190.0511.0000.0720.0510.0310.0110.0830.0380.1220.1440.0940.0710.1350.0350.0280.0220.2030.0000.0000.0000.0130.0000.0000.0000.013
income_15-24K0.1810.0240.0670.0680.0000.0100.0280.0000.0000.0160.0090.0000.0000.0090.0060.0370.0000.0050.0000.0090.0000.0000.1000.0100.1160.0360.1130.0100.0000.0530.0000.0890.0380.0330.0370.0880.0590.0080.0000.0000.0250.0350.0630.0590.0721.0000.0590.0360.0140.0960.0440.1400.1650.1080.0820.0780.0100.0550.0550.0830.0000.0000.0000.0000.0000.0100.0000.000
income_150-174K0.1180.0390.0500.0520.0000.0410.0110.0170.0000.0000.0070.0000.0070.0000.0000.0300.0000.0030.0000.0110.0120.0000.0470.0320.1170.0370.0190.0540.0540.0300.0730.0380.0000.0000.0510.0430.0000.0450.0360.0050.0270.0760.0450.0410.0510.0591.0000.0250.0060.0680.0300.1000.1170.0760.0580.0610.0540.0000.0440.0510.0000.0000.0000.0000.0000.0000.0000.000
income_175-199K0.1490.0000.0440.0430.0000.0260.0240.0000.0010.0000.0000.0000.0000.0000.0000.0200.0000.0000.0000.0000.0000.0000.0150.0210.0080.0130.0090.0340.0330.0140.0410.0660.0300.0370.0440.0000.0000.0270.0260.0470.1020.0120.0340.0250.0310.0360.0251.0000.0000.0420.0170.0630.0740.0480.0360.0190.0180.0330.0400.0000.0000.0000.0000.0000.0000.0000.0010.000
income_200-249K0.1690.0200.0180.0180.0000.0250.0000.0000.0000.0000.0000.0000.0000.0000.0000.0150.0000.0000.0000.0000.0000.0000.0690.0220.0310.0000.0000.0120.0120.0450.0040.0240.0160.0480.0180.0140.0330.0110.0000.0300.0250.0090.0120.0060.0110.0140.0060.0001.0000.0170.0000.0280.0330.0200.0130.0390.0000.0450.0160.0170.0000.0000.0000.0000.0000.0000.0000.000
income_25-34K0.1310.0120.0430.0410.0000.0350.0280.0000.0000.0230.0050.0000.0030.0000.0000.0120.0000.0000.0000.0000.0100.0000.0620.1350.1440.0000.0800.0870.0310.0810.0070.1670.0080.0300.0410.0220.0650.0240.0000.0170.0190.0270.0130.0680.0830.0960.0680.0420.0171.0000.0510.1620.1900.1240.0940.0410.0200.0000.0940.0110.0080.0000.0000.0000.0000.0000.0000.000
income_250K+0.1520.0000.0430.0420.0000.0000.0220.0000.0000.0000.0080.0000.0000.0000.0050.0000.0000.0080.0000.0000.0000.0000.1180.0540.0900.0170.0130.0410.0410.0550.0530.0290.0400.0820.0530.0000.0270.0390.0660.0640.0080.0360.0410.0310.0380.0440.0300.0170.0000.0511.0000.0760.0890.0580.0430.0680.0310.0770.0480.0000.0000.0000.0000.0000.0000.0000.0000.000
income_35-49K0.1220.0000.0440.0470.0000.0110.0000.0180.0120.0230.0000.0000.0120.0120.0080.0000.0000.0000.0000.0160.0290.0000.0460.0160.0830.0000.0220.0240.0600.0340.0390.0780.0580.0240.0060.0490.0000.0000.0290.0330.0550.0680.0690.1000.1220.1400.1000.0630.0280.1620.0761.0000.2770.1820.1380.0120.0420.0180.0220.0450.0000.0080.0000.0000.0000.0000.0120.000
income_50-74K0.1790.0000.0240.0260.0000.0120.0150.0000.0000.0000.0000.0020.0000.0000.0210.0150.0110.0000.0000.0030.0000.0000.0140.0960.0350.0030.0470.1150.0850.0190.0040.0570.0850.0560.0280.0530.0580.0450.0290.0000.0250.0270.0410.1180.1440.1650.1170.0740.0330.1900.0890.2771.0000.2140.1620.0830.0290.0640.0390.0370.0000.0090.0000.0000.0130.0000.0120.000
income_75-99K0.1490.0280.0360.0410.0560.0160.0350.0020.0000.0050.0000.0060.0100.0080.0310.0280.0000.0080.0000.0000.0100.0000.0700.0100.1520.0410.0390.0250.0960.0440.0210.0640.0670.0310.0190.0210.0000.0480.0460.0490.0430.0150.0480.0770.0940.1080.0760.0480.0200.1240.0580.1820.2141.0000.1060.0370.0270.0210.0000.0080.0000.0030.0070.0000.0000.0000.0090.000
income_Under 15K0.1380.0000.0480.0490.0000.0000.0240.0090.0040.0070.0160.0150.0000.0000.0000.0150.0000.0170.0000.0000.0000.0000.1170.0900.1120.0360.0300.1290.2400.0830.0600.0470.0150.0090.0280.0550.0460.2700.0160.0600.0760.0200.0720.0580.0710.0820.0580.0360.0130.0940.0430.1380.1620.1061.0000.0000.0170.0190.0490.0350.0000.0050.0030.0000.0000.0000.0110.000
hhsize_10.1190.0040.0390.0400.0000.0420.0000.0000.0000.0130.0000.0000.0150.0000.0110.0100.0000.0000.0000.0000.0170.0000.5810.2550.4250.0360.1580.0000.1760.4230.4190.4940.3890.2730.2250.2360.5000.0180.0540.0490.1060.0240.0340.0210.1350.0780.0610.0190.0390.0410.0680.0120.0830.0370.0001.0000.4800.2820.2070.2250.0170.0000.0000.0000.0000.0000.0000.000
hhsize_20.1280.0200.0570.0540.0000.0060.0000.0000.0090.0000.0120.0000.0140.0060.0280.0310.0000.0000.0000.0000.0190.0000.1820.1340.1960.0000.0730.0160.0670.4680.8720.1590.1380.2100.2490.2610.4860.0220.0550.0750.0050.0840.1380.0780.0350.0100.0540.0180.0000.0200.0310.0420.0290.0270.0170.4801.0000.3120.2290.2490.0000.0080.0180.0000.0000.0000.0060.000
hhsize_30.1520.0000.0220.0270.0090.0240.0000.0000.0130.0000.0000.0170.0000.0060.0000.0000.0000.0000.0000.0000.0100.0000.1400.0420.1150.0060.0450.0660.1920.4750.2720.1840.1370.8450.0700.1530.5650.0240.0350.1370.1210.0480.0000.0270.0280.0550.0000.0330.0450.0000.0770.0180.0640.0210.0190.2820.3121.0000.1350.1460.0000.0000.0080.0000.0120.0000.0000.000
hhsize_40.2230.0000.0500.0490.0000.0120.0080.0000.0030.0000.0210.0000.0000.0000.0310.0110.0250.0000.0000.0000.0090.0000.1180.0190.0350.0000.0320.1660.0930.3920.2000.1350.1010.1300.8110.0000.4150.0720.0270.0350.0060.0280.0840.0540.0220.0550.0440.0400.0160.0940.0480.0220.0390.0000.0490.2070.2290.1351.0000.1070.0000.0060.0000.0000.0000.0000.0000.000
hhsize_5+0.1320.0190.0140.0170.0250.0310.0000.0110.0000.0010.0000.0000.0000.0000.0000.0040.0000.0000.0230.0000.0000.0000.3120.1030.1620.0440.0350.0380.0520.4340.2170.1470.1090.1410.1160.9520.4500.0300.0980.0630.0390.0620.0910.0690.2030.0830.0510.0000.0170.0110.0000.0450.0370.0080.0350.2250.2490.1460.1071.0000.0110.0000.0100.0070.0000.0000.0000.007
campaign_8.00.0170.0000.0630.0640.0000.0140.0000.0110.0000.0000.0180.0270.0820.0100.0000.0560.0000.0000.0000.0000.0000.0000.0080.0000.0110.0000.0000.0000.0000.0040.0000.0140.0000.0000.0000.0200.0030.0140.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0080.0000.0000.0000.0000.0000.0170.0000.0000.0000.0111.0000.0000.0000.0000.0000.0000.4890.000
campaign_13.00.0000.1850.0500.0190.2050.0150.0040.0000.0000.0000.0000.0000.0000.0000.0000.0240.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0060.0050.0090.0000.0000.0000.0030.0000.0000.0100.0210.0240.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0080.0090.0030.0050.0000.0080.0000.0060.0000.0001.0000.0000.0000.0000.0000.5080.000
campaign_18.00.0240.0000.0380.0320.2150.0330.0090.0000.0000.0000.0000.0000.0000.0000.0000.0150.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0090.0000.0190.0270.0000.0000.0070.0030.0110.0240.0000.0480.0190.0000.0060.0000.0060.0000.0000.0000.0000.0000.0000.0000.0000.0000.0070.0030.0000.0180.0080.0000.0100.0000.0001.0000.0000.0000.0000.6370.000
campaign_25.00.0060.0000.0170.0170.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.0000.0000.0000.0000.0000.0000.0000.0000.0130.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0070.0000.0000.0001.0000.0000.0000.0000.500
campaign_26.00.0000.0000.0380.0460.0700.0040.0000.0000.0060.0000.0000.0000.0000.0000.0000.0170.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0130.0000.0000.0000.0000.0000.0110.0100.0000.0120.0000.0000.0000.0000.0000.0000.0000.0000.0000.0130.0000.0000.0000.0000.0120.0000.0000.0000.0000.0000.0001.0000.0000.2150.000
campaign_30.00.0010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0240.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0080.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0470.000
description_TypeA0.0200.0940.0320.0360.1860.0380.0090.0000.0000.0000.0000.0040.0280.0000.0000.0350.0000.0000.0000.0060.0000.0000.0020.0000.0080.0000.0000.0030.0080.0000.0140.0000.0000.0000.0000.0000.0000.0000.0320.0000.0220.0000.0000.0000.0000.0000.0000.0010.0000.0000.0000.0120.0120.0090.0110.0000.0060.0000.0000.0000.4890.5080.6370.0000.2150.0471.0000.000
description_TypeB0.0060.0000.0170.0170.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.0000.0000.0000.0000.0000.0000.0000.0000.0130.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0070.0000.0000.0000.5000.0000.0000.0001.000

Missing values

2023-05-23T14:21:42.994606image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-23T14:21:44.063149image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Unnamed: 0CURRENT_SIZE_OF_PRODUCTfirst_purchaseshelf_pricepct_discpct_retail_discpct_coupon_discdisplay_1display_2display_3display_4display_5display_6display_7display_9display_Amailer_Amailer_Cmailer_Dmailer_Fmailer_Hmailer_Jmailer_Lmarital_status_Amarital_status_Bhomeowner_Homeownerhomeowner_Probable Ownerhomeowner_Probable Renterhomeowner_Renterhhcomp_1 Adult Kidshhcomp_2 Adults Kidshhcomp_2 Adults No Kidshhcomp_Single Femalehhcomp_Single Malekid_category_1kid_category_2kid_category_3+kid_category_None/Unknownage_19-24age_25-34age_35-44age_45-54age_55-64age_65+income_100-124Kincome_125-149Kincome_15-24Kincome_150-174Kincome_175-199Kincome_200-249Kincome_25-34Kincome_250K+income_35-49Kincome_50-74Kincome_75-99Kincome_Under 15Khhsize_1hhsize_2hhsize_3hhsize_4hhsize_5+campaign_8.0campaign_13.0campaign_18.0campaign_25.0campaign_26.0campaign_30.0description_TypeAdescription_TypeBproduct_size
00True1.880.000000-0.000000-0.000000000000000001010000010000010000010000000010000100000000000NaN
1118 OZTrue10.990.000000-0.000000-0.00000000001000000101000001000001000001000000001000010000000000018
226 OZTrue1.790.000000-0.000000-0.0000000000000000010100000100000100000100000000100001000000000006
336 OZFalse1.790.000000-0.000000-0.0000000000000000010100000100000100000100000000100001000000000006
443.5 OZFalse1.790.1005590.100559-0.0000000000000000010100000100000100000100000000100001000000000003.5
553.5 OZFalse1.790.1620110.162011-0.0000000000000000010100000100000100000100000000100001000000000003.5
663.5 OZFalse1.790.1620110.162011-0.0000000000000000010100000100000100000100000000100001000000000003.5
773.5 OZFalse1.790.1005590.100559-0.0000000000000000010100000100000100000100000000100001000000000003.5
883.5 OZFalse1.790.1005590.100559-0.0000000000000000010100000100000100000100000000100001000000000003.5
995.5 OZTrue1.790.000000-0.000000-0.0000000000000000010100000100000100000100000000100001000000000005.5
Unnamed: 0CURRENT_SIZE_OF_PRODUCTfirst_purchaseshelf_pricepct_discpct_retail_discpct_coupon_discdisplay_1display_2display_3display_4display_5display_6display_7display_9display_Amailer_Amailer_Cmailer_Dmailer_Fmailer_Hmailer_Jmailer_Lmarital_status_Amarital_status_Bhomeowner_Homeownerhomeowner_Probable Ownerhomeowner_Probable Renterhomeowner_Renterhhcomp_1 Adult Kidshhcomp_2 Adults Kidshhcomp_2 Adults No Kidshhcomp_Single Femalehhcomp_Single Malekid_category_1kid_category_2kid_category_3+kid_category_None/Unknownage_19-24age_25-34age_35-44age_45-54age_55-64age_65+income_100-124Kincome_125-149Kincome_15-24Kincome_150-174Kincome_175-199Kincome_200-249Kincome_25-34Kincome_250K+income_35-49Kincome_50-74Kincome_75-99Kincome_Under 15Khhsize_1hhsize_2hhsize_3hhsize_4hhsize_5+campaign_8.0campaign_13.0campaign_18.0campaign_25.0campaign_26.0campaign_30.0description_TypeAdescription_TypeBproduct_size
151972739610.78 OZTrue3.190.000000-0.000000-0.00000000000000000001000001000001010000000000000100010000000000010.78
15198273974.25 OZTrue1.490.3288590.328859-0.0100000000100000000100000100000101000000000000010001000000000004.25
151992739814 OZTrue3.190.2163010.216301-0.00010000000000000001000001000001010000000000000100010000000000014
152002739919.75 OZTrue4.890.000000-0.000000-0.00000000000000000001000001000001010000000000000100010000000000019.75
152012740012 OZTrue2.790.1039430.103943-0.00000000000000000000000010001000010000000000000001001000000000012
152022740114 OZTrue2.290.000000-0.000000-0.00000000000000000000000010001000010000000000000001001000000000014
152032740220 OZTrue1.500.000000-0.000000-0.00000000000000000000000010001000010000000000000001001000000000020
152042740310.5 OZFalse3.190.3761760.376176-0.00000000000000000000000010001000010000000000000001001000000000010.5
15205274044.4 OZTrue1.490.000000-0.000000-0.0000000000000000000000001000100001000000000000000100100000000004.4
15206274051.5 OZTrue1.990.2462310.246231-0.0000000000000000000000001000100001000000000000000100100000000001.5